_make_data_gen() and start_enqueueing_threads()

I’m not sure I understand what is happening in inputs/kitti_seg_input.py here: https://github.com/MarvinTeichmann/KittiSeg/blob/master/inputs/kitti_seg_input.py#L169-L173.

elif phase == 'train':
    yield jitter_input(hypes, image, gt_image)
    yield jitter_input(hypes, np.fliplr(image), np.fliplr(gt_image))

So, we are yielding 2 generators to the variable, gen, in L359:

gen = _make_data_gen(hypes, phase, data_dir)
gen.next() # but then why do we just iterate over it? 

But then why do we do the gen.next() in the following line, without making use of the returned value? What is the purpose of this portion of the code, in start_enqueueing_threads()?

[MarvinTeichmann]

Yes, data augmentation like random flipping is used to increasing the amount of effectively available training data.

Other forms of data augmentation applied is random aspect ratio augmentation (random resize, random crop) and color augmentation (random brightness, random contrast, random saturation and random hue).

[MarvinTeichmann]

If you use batch_size = 1 (default) you can train the network with variable image sizes (default and recommended).

For batch_sizes > 1 all inputs need the same size. That can be achieved by setting reseize_image or crop_patch to true. Resize_image will perform bilinear resize after the augmentation (if you use both random resize and random crop this means that still meaningful augmentation is done) . crop_patch will crop a random patch of fixed size on the image.